Robustness.
Model robustness is an increasingly important issue in modern machine learning. Robustness with respect to adversarial examples [1]} can be achieved by adversarial training [2]}, [3]}, [4]}. Several works present theoretical justifications to observed robustness and how data augmentation can improve it [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}. Relatedly, [18]}, [19]}, [20]} investigate how noise injection can be used to improve robustness. Parallel to this line of work, we provide theory to understand how NFM can improve robustness. Also related to this line of work is the study of the trade-offs between robustness and accuracy [21]}, [22]}, [23]}, [24]}, [25]}, [26]}, [27]}. There are also attempts to study generalization in terms of robustness [28]}, [29]}, [30]}.
